Overview

Dataset statistics

Number of variables29
Number of observations73
Missing cells85
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 KiB
Average record size in memory233.8 B

Variable types

Numeric9
Categorical20

Alerts

type has constant value "regular" Constant
airdate has constant value "2020-12-29" Constant
url has a high cardinality: 73 distinct values High cardinality
name has a high cardinality: 60 distinct values High cardinality
_links_self_href has a high cardinality: 73 distinct values High cardinality
season is highly correlated with _embedded_show_updatedHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_updated is highly correlated with seasonHigh correlation
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with season and 2 other fieldsHigh correlation
runtime is highly correlated with number and 2 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 3 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_summary and 15 other fieldsHigh correlation
summary is highly correlated with url and 4 other fieldsHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
url is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
image is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_summary and 11 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
name is highly correlated with url and 4 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
_links_self_href is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 15 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 18 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 16 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
id is highly correlated with url and 21 other fieldsHigh correlation
url is highly correlated with id and 25 other fieldsHigh correlation
name is highly correlated with url and 14 other fieldsHigh correlation
season is highly correlated with url and 14 other fieldsHigh correlation
number is highly correlated with url and 15 other fieldsHigh correlation
airtime is highly correlated with id and 19 other fieldsHigh correlation
airstamp is highly correlated with id and 20 other fieldsHigh correlation
runtime is highly correlated with id and 21 other fieldsHigh correlation
image is highly correlated with id and 23 other fieldsHigh correlation
summary is highly correlated with url and 14 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 24 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_status is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 22 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 14 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 25 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with id and 11 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 23 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 18 other fieldsHigh correlation
_links_self_href is highly correlated with id and 25 other fieldsHigh correlation
runtime has 2 (2.7%) missing values Missing
image has 61 (83.6%) missing values Missing
_embedded_show_runtime has 20 (27.4%) missing values Missing
_embedded_show_averageRuntime has 2 (2.7%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
image is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique

Reproduction

Analysis started2022-05-10 02:23:02.718088
Analysis finished2022-05-10 02:23:27.514664
Duration24.8 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035419.658
Minimum1960500
Maximum2324424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:27.592011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1960500
5-th percentile1976050.6
Q11989717
median1997183
Q32068352
95-th percentile2197357.4
Maximum2324424
Range363924
Interquartile range (IQR)78635

Descriptive statistics

Standard deviation82374.69478
Coefficient of variation (CV)0.04047061964
Kurtosis4.453850726
Mean2035419.658
Median Absolute Deviation (MAD)10848
Skewness2.143140629
Sum148585635
Variance6785590339
MonotonicityNot monotonic
2022-05-09T21:23:27.705089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20077561
 
1.4%
21761461
 
1.4%
19938271
 
1.4%
19883031
 
1.4%
19880731
 
1.4%
19880721
 
1.4%
19852051
 
1.4%
19849591
 
1.4%
19849581
 
1.4%
19760511
 
1.4%
Other values (63)63
86.3%
ValueCountFrequency (%)
19605001
1.4%
19645691
1.4%
19680041
1.4%
19760501
1.4%
19760511
1.4%
19774211
1.4%
19793061
1.4%
19804061
1.4%
19804071
1.4%
19849581
1.4%
ValueCountFrequency (%)
23244241
1.4%
23244231
1.4%
23181151
1.4%
22044531
1.4%
21926271
1.4%
21761461
1.4%
21650091
1.4%
21614181
1.4%
21264821
1.4%
21264811
1.4%

url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
https://www.tvmaze.com/episodes/2007756/stand-up-autsajd-1x11-pavel-dedisev-17-minut-serebra
 
1
https://www.tvmaze.com/episodes/2176146/scishow-2020-12-29-the-fly-that-lays-eggs-in-toad-nostrils
 
1
https://www.tvmaze.com/episodes/1993827/the-case-solver-1x21-episode-21
 
1
https://www.tvmaze.com/episodes/1988303/nwa-shockwave-1x05-episode-5
 
1
https://www.tvmaze.com/episodes/1988073/forever-love-1x22-episode-22
 
1
Other values (68)
68 

Length

Max length124
Median length96
Mean length81.36986301
Min length59

Characters and Unicode

Total characters5940
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2007756/stand-up-autsajd-1x11-pavel-dedisev-17-minut-serebra
2nd rowhttps://www.tvmaze.com/episodes/1987860/blic-krik-1x12-12-nurlan-saburov-t-fest-garik-oganisan-rustam-reptiloid-emir-kasokov
3rd rowhttps://www.tvmaze.com/episodes/2008031/lab-s-antonom-belaevym-2x10-therr-maitz
4th rowhttps://www.tvmaze.com/episodes/1964569/core-sense-1x13-episode-13
5th rowhttps://www.tvmaze.com/episodes/2052513/wu-shen-zhu-zai-1x88-episode-88

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2007756/stand-up-autsajd-1x11-pavel-dedisev-17-minut-serebra1
 
1.4%
https://www.tvmaze.com/episodes/2176146/scishow-2020-12-29-the-fly-that-lays-eggs-in-toad-nostrils1
 
1.4%
https://www.tvmaze.com/episodes/1993827/the-case-solver-1x21-episode-211
 
1.4%
https://www.tvmaze.com/episodes/1988303/nwa-shockwave-1x05-episode-51
 
1.4%
https://www.tvmaze.com/episodes/1988073/forever-love-1x22-episode-221
 
1.4%
https://www.tvmaze.com/episodes/1988072/forever-love-1x21-episode-211
 
1.4%
https://www.tvmaze.com/episodes/1985205/handmade-love-1x06-meet-me-in-your-past1
 
1.4%
https://www.tvmaze.com/episodes/1984959/dream-detective-1x20-episode-201
 
1.4%
https://www.tvmaze.com/episodes/1984958/dream-detective-1x19-episode-191
 
1.4%
https://www.tvmaze.com/episodes/1976051/twisted-fate-of-love-1x40-episode-401
 
1.4%
Other values (63)63
86.3%

Length

2022-05-09T21:23:27.846295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2007756/stand-up-autsajd-1x11-pavel-dedisev-17-minut-serebra1
 
1.4%
https://www.tvmaze.com/episodes/1987860/blic-krik-1x12-12-nurlan-saburov-t-fest-garik-oganisan-rustam-reptiloid-emir-kasokov1
 
1.4%
https://www.tvmaze.com/episodes/2008031/lab-s-antonom-belaevym-2x10-therr-maitz1
 
1.4%
https://www.tvmaze.com/episodes/1964569/core-sense-1x13-episode-131
 
1.4%
https://www.tvmaze.com/episodes/2052513/wu-shen-zhu-zai-1x88-episode-881
 
1.4%
https://www.tvmaze.com/episodes/1993658/7-days-of-romance-2x03-episode-31
 
1.4%
https://www.tvmaze.com/episodes/2096302/no-turning-back-romance-1x07-71
 
1.4%
https://www.tvmaze.com/episodes/2324423/unique-lady-2x11-episode-111
 
1.4%
https://www.tvmaze.com/episodes/2324424/unique-lady-2x12-episode-121
 
1.4%
https://www.tvmaze.com/episodes/2068352/doomsday-awakening-2x03-episode-31
 
1.4%
Other values (63)63
86.3%

Most occurring characters

ValueCountFrequency (%)
e519
 
8.7%
-464
 
7.8%
/365
 
6.1%
t362
 
6.1%
s359
 
6.0%
o296
 
5.0%
w245
 
4.1%
i239
 
4.0%
a238
 
4.0%
p218
 
3.7%
Other values (30)2635
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4016
67.6%
Decimal Number876
 
14.7%
Other Punctuation584
 
9.8%
Dash Punctuation464
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e519
12.9%
t362
 
9.0%
s359
 
8.9%
o296
 
7.4%
w245
 
6.1%
i239
 
6.0%
a238
 
5.9%
p218
 
5.4%
m195
 
4.9%
d171
 
4.3%
Other values (16)1174
29.2%
Decimal Number
ValueCountFrequency (%)
1180
20.5%
2160
18.3%
0129
14.7%
9101
11.5%
760
 
6.8%
856
 
6.4%
352
 
5.9%
451
 
5.8%
646
 
5.3%
541
 
4.7%
Other Punctuation
ValueCountFrequency (%)
/365
62.5%
.146
 
25.0%
:73
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4016
67.6%
Common1924
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e519
12.9%
t362
 
9.0%
s359
 
8.9%
o296
 
7.4%
w245
 
6.1%
i239
 
6.0%
a238
 
5.9%
p218
 
5.4%
m195
 
4.9%
d171
 
4.3%
Other values (16)1174
29.2%
Common
ValueCountFrequency (%)
-464
24.1%
/365
19.0%
1180
 
9.4%
2160
 
8.3%
.146
 
7.6%
0129
 
6.7%
9101
 
5.2%
:73
 
3.8%
760
 
3.1%
856
 
2.9%
Other values (4)190
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5940
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e519
 
8.7%
-464
 
7.8%
/365
 
6.1%
t362
 
6.1%
s359
 
6.0%
o296
 
5.0%
w245
 
4.1%
i239
 
4.0%
a238
 
4.0%
p218
 
3.7%
Other values (30)2635
44.4%

name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct60
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size712.0 B
Episode 3
Episode 12
 
3
Episode 2
 
2
Episode 11
 
2
Episode 5
 
2
Other values (55)
58 

Length

Max length75
Median length41
Mean length18.30136986
Min length1

Characters and Unicode

Total characters1336
Distinct characters124
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)71.2%

Sample

1st rowПавел Дедищев "17 минут серебра"
2nd row#12: НУРЛАН САБУРОВ, T-Fest, ГАРИК ОГАНИСЯН, РУСТАМ РЕПТИЛОИД, ЭМИР КАШОКОВ
3rd rowTherr Maitz
4th rowEpisode 13
5th rowEpisode 88

Common Values

ValueCountFrequency (%)
Episode 36
 
8.2%
Episode 123
 
4.1%
Episode 22
 
2.7%
Episode 112
 
2.7%
Episode 52
 
2.7%
Episode 212
 
2.7%
Episode 222
 
2.7%
Episode 102
 
2.7%
AEW Dark 671
 
1.4%
World Famous Voice Actor Clancy Brown1
 
1.4%
Other values (50)50
68.5%

Length

2022-05-09T21:23:27.972627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode35
 
14.8%
the8
 
3.4%
36
 
2.5%
124
 
1.7%
24
 
1.7%
20204
 
1.7%
in3
 
1.3%
of3
 
1.3%
odcinek2
 
0.8%
er2
 
0.8%
Other values (155)166
70.0%

Most occurring characters

ValueCountFrequency (%)
164
 
12.3%
e117
 
8.8%
o78
 
5.8%
i72
 
5.4%
s63
 
4.7%
r59
 
4.4%
d57
 
4.3%
a53
 
4.0%
p42
 
3.1%
n38
 
2.8%
Other values (114)593
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter831
62.2%
Uppercase Letter200
 
15.0%
Space Separator164
 
12.3%
Decimal Number93
 
7.0%
Other Punctuation24
 
1.8%
Other Letter16
 
1.2%
Dash Punctuation4
 
0.3%
Close Punctuation2
 
0.1%
Open Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e117
14.1%
o78
 
9.4%
i72
 
8.7%
s63
 
7.6%
r59
 
7.1%
d57
 
6.9%
a53
 
6.4%
p42
 
5.1%
n38
 
4.6%
t37
 
4.5%
Other values (38)215
25.9%
Uppercase Letter
ValueCountFrequency (%)
E37
 
18.5%
T15
 
7.5%
P9
 
4.5%
А8
 
4.0%
O7
 
3.5%
M7
 
3.5%
H6
 
3.0%
L6
 
3.0%
Р6
 
3.0%
И5
 
2.5%
Other values (33)94
47.0%
Other Letter
ValueCountFrequency (%)
و3
18.8%
ا2
12.5%
1
 
6.2%
1
 
6.2%
ب1
 
6.2%
ز1
 
6.2%
م1
 
6.2%
ی1
 
6.2%
ل1
 
6.2%
گ1
 
6.2%
Other values (3)3
18.8%
Decimal Number
ValueCountFrequency (%)
228
30.1%
120
21.5%
012
12.9%
312
12.9%
95
 
5.4%
64
 
4.3%
54
 
4.3%
73
 
3.2%
43
 
3.2%
82
 
2.2%
Other Punctuation
ValueCountFrequency (%)
:9
37.5%
,9
37.5%
"2
 
8.3%
#2
 
8.3%
!1
 
4.2%
.1
 
4.2%
Space Separator
ValueCountFrequency (%)
164
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin911
68.2%
Common289
 
21.6%
Cyrillic120
 
9.0%
Arabic14
 
1.0%
Han2
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e117
 
12.8%
o78
 
8.6%
i72
 
7.9%
s63
 
6.9%
r59
 
6.5%
d57
 
6.3%
a53
 
5.8%
p42
 
4.6%
n38
 
4.2%
E37
 
4.1%
Other values (38)295
32.4%
Cyrillic
ValueCountFrequency (%)
е9
 
7.5%
А8
 
6.7%
а6
 
5.0%
Р6
 
5.0%
И5
 
4.2%
и5
 
4.2%
О5
 
4.2%
р5
 
4.2%
Н4
 
3.3%
с4
 
3.3%
Other values (33)63
52.5%
Common
ValueCountFrequency (%)
164
56.7%
228
 
9.7%
120
 
6.9%
012
 
4.2%
312
 
4.2%
:9
 
3.1%
,9
 
3.1%
95
 
1.7%
-4
 
1.4%
64
 
1.4%
Other values (10)22
 
7.6%
Arabic
ValueCountFrequency (%)
و3
21.4%
ا2
14.3%
ب1
 
7.1%
ز1
 
7.1%
م1
 
7.1%
ی1
 
7.1%
ل1
 
7.1%
گ1
 
7.1%
ر1
 
7.1%
ه1
 
7.1%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1198
89.7%
Cyrillic120
 
9.0%
Arabic14
 
1.0%
None2
 
0.1%
CJK2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
164
 
13.7%
e117
 
9.8%
o78
 
6.5%
i72
 
6.0%
s63
 
5.3%
r59
 
4.9%
d57
 
4.8%
a53
 
4.4%
p42
 
3.5%
n38
 
3.2%
Other values (57)455
38.0%
Cyrillic
ValueCountFrequency (%)
е9
 
7.5%
А8
 
6.7%
а6
 
5.0%
Р6
 
5.0%
И5
 
4.2%
и5
 
4.2%
О5
 
4.2%
р5
 
4.2%
Н4
 
3.3%
с4
 
3.3%
Other values (33)63
52.5%
Arabic
ValueCountFrequency (%)
و3
21.4%
ا2
14.3%
ب1
 
7.1%
ز1
 
7.1%
م1
 
7.1%
ی1
 
7.1%
ل1
 
7.1%
گ1
 
7.1%
ر1
 
7.1%
ه1
 
7.1%
None
ValueCountFrequency (%)
å2
100.0%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.0958904
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:28.067302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation634.7682465
Coefficient of variation (CV)2.845270907
Kurtosis4.640533316
Mean223.0958904
Median Absolute Deviation (MAD)0
Skewness2.552211381
Sum16286
Variance402930.7268
MonotonicityNot monotonic
2022-05-09T21:23:28.165736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
144
60.3%
214
 
19.2%
20208
 
11.0%
43
 
4.1%
32
 
2.7%
51
 
1.4%
311
 
1.4%
ValueCountFrequency (%)
144
60.3%
214
 
19.2%
32
 
2.7%
43
 
4.1%
51
 
1.4%
311
 
1.4%
20208
 
11.0%
ValueCountFrequency (%)
20208
 
11.0%
311
 
1.4%
51
 
1.4%
43
 
4.1%
32
 
2.7%
214
 
19.2%
144
60.3%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.93150685
Minimum1
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:28.284711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q322
95-th percentile110.4
Maximum356
Range355
Interquartile range (IQR)19

Descriptive statistics

Standard deviation67.1981582
Coefficient of variation (CV)2.245064324
Kurtosis15.80180599
Mean29.93150685
Median Absolute Deviation (MAD)7
Skewness3.96694672
Sum2185
Variance4515.592466
MonotonicityNot monotonic
2022-05-09T21:23:28.364659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
39
 
12.3%
16
 
8.2%
26
 
8.2%
54
 
5.5%
124
 
5.5%
64
 
5.5%
44
 
5.5%
113
 
4.1%
103
 
4.1%
72
 
2.7%
Other values (22)28
38.4%
ValueCountFrequency (%)
16
8.2%
26
8.2%
39
12.3%
44
5.5%
54
5.5%
64
5.5%
72
 
2.7%
103
 
4.1%
113
 
4.1%
124
5.5%
ValueCountFrequency (%)
3561
1.4%
3201
1.4%
3191
1.4%
1441
1.4%
881
1.4%
701
1.4%
591
1.4%
532
2.7%
522
2.7%
401
1.4%

type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size712.0 B
regular
73 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters511
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular73
100.0%

Length

2022-05-09T21:23:28.582228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:28.661045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular73
100.0%

Most occurring characters

ValueCountFrequency (%)
r146
28.6%
e73
14.3%
g73
14.3%
u73
14.3%
l73
14.3%
a73
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter511
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r146
28.6%
e73
14.3%
g73
14.3%
u73
14.3%
l73
14.3%
a73
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin511
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r146
28.6%
e73
14.3%
g73
14.3%
u73
14.3%
l73
14.3%
a73
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII511
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r146
28.6%
e73
14.3%
g73
14.3%
u73
14.3%
l73
14.3%
a73
14.3%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size712.0 B
2020-12-29
73 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters730
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-29
2nd row2020-12-29
3rd row2020-12-29
4th row2020-12-29
5th row2020-12-29

Common Values

ValueCountFrequency (%)
2020-12-2973
100.0%

Length

2022-05-09T21:23:28.750698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:28.829045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2973
100.0%

Most occurring characters

ValueCountFrequency (%)
2292
40.0%
0146
20.0%
-146
20.0%
173
 
10.0%
973
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number584
80.0%
Dash Punctuation146
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2292
50.0%
0146
25.0%
173
 
12.5%
973
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2292
40.0%
0146
20.0%
-146
20.0%
173
 
10.0%
973
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2292
40.0%
0146
20.0%
-146
20.0%
173
 
10.0%
973
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size712.0 B
nan
51 
20:00
12:00
 
4
21:00
 
4
10:00
 
2
Other values (5)
 
5

Length

Max length5
Median length3
Mean length3.602739726
Min length3

Characters and Unicode

Total characters263
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.8%

Sample

1st row12:00
2nd row12:00
3rd rownan
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
nan51
69.9%
20:007
 
9.6%
12:004
 
5.5%
21:004
 
5.5%
10:002
 
2.7%
08:001
 
1.4%
17:001
 
1.4%
22:001
 
1.4%
19:001
 
1.4%
18:001
 
1.4%

Length

2022-05-09T21:23:28.907196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:29.026773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan51
69.9%
20:007
 
9.6%
12:004
 
5.5%
21:004
 
5.5%
10:002
 
2.7%
08:001
 
1.4%
17:001
 
1.4%
22:001
 
1.4%
19:001
 
1.4%
18:001
 
1.4%

Most occurring characters

ValueCountFrequency (%)
n102
38.8%
054
20.5%
a51
19.4%
:22
 
8.4%
217
 
6.5%
113
 
4.9%
82
 
0.8%
71
 
0.4%
91
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter153
58.2%
Decimal Number88
33.5%
Other Punctuation22
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
054
61.4%
217
 
19.3%
113
 
14.8%
82
 
2.3%
71
 
1.1%
91
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
n102
66.7%
a51
33.3%
Other Punctuation
ValueCountFrequency (%)
:22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin153
58.2%
Common110
41.8%

Most frequent character per script

Common
ValueCountFrequency (%)
054
49.1%
:22
20.0%
217
 
15.5%
113
 
11.8%
82
 
1.8%
71
 
0.9%
91
 
0.9%
Latin
ValueCountFrequency (%)
n102
66.7%
a51
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n102
38.8%
054
20.5%
a51
19.4%
:22
 
8.4%
217
 
6.5%
113
 
4.9%
82
 
0.8%
71
 
0.4%
91
 
0.4%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size712.0 B
2020-12-29T12:00:00+00:00
30 
2020-12-29T11:00:00+00:00
10 
2020-12-29T04:00:00+00:00
2020-12-29T06:30:00+00:00
2020-12-29T17:00:00+00:00
Other values (8)
15 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1825
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)5.5%

Sample

1st row2020-12-29T00:00:00+00:00
2nd row2020-12-29T00:00:00+00:00
3rd row2020-12-29T00:00:00+00:00
4th row2020-12-29T02:00:00+00:00
5th row2020-12-29T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-29T12:00:00+00:0030
41.1%
2020-12-29T11:00:00+00:0010
 
13.7%
2020-12-29T04:00:00+00:007
 
9.6%
2020-12-29T06:30:00+00:007
 
9.6%
2020-12-29T17:00:00+00:004
 
5.5%
2020-12-29T21:00:00+00:004
 
5.5%
2020-12-29T00:00:00+00:003
 
4.1%
2020-12-29T02:00:00+00:002
 
2.7%
2020-12-29T03:00:00+00:002
 
2.7%
2020-12-29T04:30:00+00:001
 
1.4%
Other values (3)3
 
4.1%

Length

2022-05-09T21:23:29.136950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-29t12:00:00+00:0030
41.1%
2020-12-29t11:00:00+00:0010
 
13.7%
2020-12-29t04:00:00+00:007
 
9.6%
2020-12-29t06:30:00+00:007
 
9.6%
2020-12-29t17:00:00+00:004
 
5.5%
2020-12-29t21:00:00+00:004
 
5.5%
2020-12-29t00:00:00+00:003
 
4.1%
2020-12-29t02:00:00+00:002
 
2.7%
2020-12-29t03:00:00+00:002
 
2.7%
2020-12-29t04:30:00+00:001
 
1.4%
Other values (3)3
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0748
41.0%
2328
18.0%
:219
 
12.0%
-146
 
8.0%
1133
 
7.3%
974
 
4.1%
T73
 
4.0%
+73
 
4.0%
310
 
0.5%
48
 
0.4%
Other values (4)13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1314
72.0%
Other Punctuation219
 
12.0%
Dash Punctuation146
 
8.0%
Uppercase Letter73
 
4.0%
Math Symbol73
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0748
56.9%
2328
25.0%
1133
 
10.1%
974
 
5.6%
310
 
0.8%
48
 
0.6%
67
 
0.5%
74
 
0.3%
81
 
0.1%
51
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:219
100.0%
Dash Punctuation
ValueCountFrequency (%)
-146
100.0%
Uppercase Letter
ValueCountFrequency (%)
T73
100.0%
Math Symbol
ValueCountFrequency (%)
+73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1752
96.0%
Latin73
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0748
42.7%
2328
18.7%
:219
 
12.5%
-146
 
8.3%
1133
 
7.6%
974
 
4.2%
+73
 
4.2%
310
 
0.6%
48
 
0.5%
67
 
0.4%
Other values (3)6
 
0.3%
Latin
ValueCountFrequency (%)
T73
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0748
41.0%
2328
18.0%
:219
 
12.0%
-146
 
8.0%
1133
 
7.3%
974
 
4.1%
T73
 
4.0%
+73
 
4.0%
310
 
0.5%
48
 
0.4%
Other values (4)13
 
0.7%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)39.4%
Missing2
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean38.53521127
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:29.215006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q121.5
median30
Q345
95-th percentile90
Maximum120
Range116
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation26.02132696
Coefficient of variation (CV)0.6752610432
Kurtosis2.655974373
Mean38.53521127
Median Absolute Deviation (MAD)15
Skewness1.567486729
Sum2736
Variance677.1094567
MonotonicityNot monotonic
2022-05-09T21:23:29.329644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4513
17.8%
3010
13.7%
605
 
6.8%
904
 
5.5%
274
 
5.5%
1203
 
4.1%
193
 
4.1%
183
 
4.1%
123
 
4.1%
252
 
2.7%
Other values (18)21
28.8%
ValueCountFrequency (%)
41
 
1.4%
52
2.7%
81
 
1.4%
101
 
1.4%
123
4.1%
152
2.7%
161
 
1.4%
183
4.1%
193
4.1%
201
 
1.4%
ValueCountFrequency (%)
1203
 
4.1%
904
 
5.5%
605
 
6.8%
481
 
1.4%
4513
17.8%
431
 
1.4%
411
 
1.4%
402
 
2.7%
381
 
1.4%
371
 
1.4%

image
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct12
Distinct (%)100.0%
Missing61
Missing (%)83.6%
Memory size712.0 B
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737210.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737210.jpg'}
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/728291.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/728291.jpg'}
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/293/733849.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/293/733849.jpg'}
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/729339.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/729339.jpg'}
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/292/730358.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/292/730358.jpg'}
Other values (7)

Length

Max length176
Median length176
Mean length176
Min length176

Characters and Unicode

Total characters2112
Distinct characters38
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737210.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737210.jpg'}
2nd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/728291.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/728291.jpg'}
3rd row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/293/733849.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/293/733849.jpg'}
4th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/729339.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/729339.jpg'}
5th row{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/292/730358.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/292/730358.jpg'}

Common Values

ValueCountFrequency (%)
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737210.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737210.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/728291.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/728291.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/293/733849.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/293/733849.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/729339.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/729339.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/292/730358.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/292/730358.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/728205.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/728205.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724614.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724614.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727577.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727577.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727732.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727732.jpg'}1
 
1.4%
{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727731.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727731.jpg'}1
 
1.4%
Other values (2)2
 
2.7%
(Missing)61
83.6%

Length

2022-05-09T21:23:29.437611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medium12
25.0%
original12
25.0%
https://static.tvmaze.com/uploads/images/medium_landscape/289/724614.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727727.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/727729.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727729.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/727731.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727731.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/727732.jpg1
 
2.1%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727732.jpg1
 
2.1%
Other values (16)16
33.3%

Most occurring characters

ValueCountFrequency (%)
/168
 
8.0%
a144
 
6.8%
t132
 
6.2%
m120
 
5.7%
i120
 
5.7%
s108
 
5.1%
e96
 
4.5%
'96
 
4.5%
o84
 
4.0%
p84
 
4.0%
Other values (28)960
45.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1416
67.0%
Other Punctuation396
 
18.8%
Decimal Number216
 
10.2%
Space Separator36
 
1.7%
Connector Punctuation24
 
1.1%
Close Punctuation12
 
0.6%
Open Punctuation12
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a144
 
10.2%
t132
 
9.3%
m120
 
8.5%
i120
 
8.5%
s108
 
7.6%
e96
 
6.8%
o84
 
5.9%
p84
 
5.9%
g72
 
5.1%
c72
 
5.1%
Other values (9)384
27.1%
Decimal Number
ValueCountFrequency (%)
256
25.9%
750
23.1%
934
15.7%
124
11.1%
320
 
9.3%
810
 
4.6%
48
 
3.7%
06
 
2.8%
56
 
2.8%
62
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/168
42.4%
'96
24.2%
.72
18.2%
:48
 
12.1%
,12
 
3.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Connector Punctuation
ValueCountFrequency (%)
_24
100.0%
Close Punctuation
ValueCountFrequency (%)
}12
100.0%
Open Punctuation
ValueCountFrequency (%)
{12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1416
67.0%
Common696
33.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/168
24.1%
'96
13.8%
.72
10.3%
256
 
8.0%
750
 
7.2%
:48
 
6.9%
36
 
5.2%
934
 
4.9%
124
 
3.4%
_24
 
3.4%
Other values (9)88
12.6%
Latin
ValueCountFrequency (%)
a144
 
10.2%
t132
 
9.3%
m120
 
8.5%
i120
 
8.5%
s108
 
7.6%
e96
 
6.8%
o84
 
5.9%
p84
 
5.9%
g72
 
5.1%
c72
 
5.1%
Other values (9)384
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/168
 
8.0%
a144
 
6.8%
t132
 
6.2%
m120
 
5.7%
i120
 
5.7%
s108
 
5.1%
e96
 
4.5%
'96
 
4.5%
o84
 
4.0%
p84
 
4.0%
Other values (28)960
45.5%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size712.0 B
nan
64 
<p>Mike and the @Returning the Favor crew surprise Shelina Moreda, a motorcycle racing/model/dairy farmer's daughter who is saving animals from the wildfires and other situations in Northern California.</p>
 
1
<p>Clancy Brown (Shawshank, SpongeBob) joins me this week to share his personal experience with impostor syndrome throughout his career from its inception to highlights like The Shawshank Redemption. </p><p>Clancy talks about how the industry had changed over the decades and how he feels blessed to have found voice acting for the presence it allows him to keep. </p><p>We also get into some good ole Lex talk, how fame can drastically change a set, and even his personal experience working with the late great Sean Connery.</p>
 
1
<p>James comes out to his mother about his relationship with Sky.  The two spend quality time together at the night market and around the city.  </p>
 
1
<p>Observed at a psychiatric hospital and composed in court, the accused remains a locked box and offers few clues. Now the therapist revisits his case.</p>
 
1
Other values (5)
 
5

Length

Max length529
Median length3
Mean length29.69863014
Min length3

Characters and Unicode

Total characters2168
Distinct characters52
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)12.3%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan64
87.7%
<p>Mike and the @Returning the Favor crew surprise Shelina Moreda, a motorcycle racing/model/dairy farmer's daughter who is saving animals from the wildfires and other situations in Northern California.</p>1
 
1.4%
<p>Clancy Brown (Shawshank, SpongeBob) joins me this week to share his personal experience with impostor syndrome throughout his career from its inception to highlights like The Shawshank Redemption. </p><p>Clancy talks about how the industry had changed over the decades and how he feels blessed to have found voice acting for the presence it allows him to keep. </p><p>We also get into some good ole Lex talk, how fame can drastically change a set, and even his personal experience working with the late great Sean Connery.</p>1
 
1.4%
<p>James comes out to his mother about his relationship with Sky.  The two spend quality time together at the night market and around the city.  </p>1
 
1.4%
<p>Observed at a psychiatric hospital and composed in court, the accused remains a locked box and offers few clues. Now the therapist revisits his case.</p>1
 
1.4%
<p>Claire and Eric have seemingly moved on with their lives, but a chance encounter brings new truths to light.<br /> </p>1
 
1.4%
<p>The first episode examines how the Beckhams changed the model of the celebrity power couple, while the rise of reality TV led to a gigantic influx of people gaining overnight fame, fed by a press that saw an easy source of material. The programme also examines how leaked sex tapes changed the nature of celebrity scandal.</p>1
 
1.4%
<p>Charting the story of celebrity in the late noughties, this film looks at how different groups cashed in on the public's insatiable appetite for access to the famous.</p>1
 
1.4%
<p>This film tells the story of how celebrities capitalised on a digital revolution in order to sidestep the traditional routes to fame.</p>1
 
1.4%
<p>Charting the last five tumultuous years, this final episode takes us to the end of 2020 and interrogates the methods used by celebrities to get everything they want.</p>1
 
1.4%

Length

2022-05-09T21:23:29.531713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:29.673010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan64
 
16.7%
the28
 
7.3%
to12
 
3.1%
and9
 
2.3%
a8
 
2.1%
of8
 
2.1%
how7
 
1.8%
his6
 
1.6%
in5
 
1.3%
with4
 
1.0%
Other values (203)232
60.6%

Most occurring characters

ValueCountFrequency (%)
307
14.2%
n217
 
10.0%
e200
 
9.2%
a183
 
8.4%
t140
 
6.5%
i117
 
5.4%
o113
 
5.2%
s103
 
4.8%
h96
 
4.4%
r89
 
4.1%
Other values (42)603
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1728
79.7%
Space Separator312
 
14.4%
Math Symbol46
 
2.1%
Other Punctuation41
 
1.9%
Uppercase Letter35
 
1.6%
Decimal Number4
 
0.2%
Close Punctuation1
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n217
12.6%
e200
11.6%
a183
10.6%
t140
 
8.1%
i117
 
6.8%
o113
 
6.5%
s103
 
6.0%
h96
 
5.6%
r89
 
5.2%
l63
 
3.6%
Other values (15)407
23.6%
Uppercase Letter
ValueCountFrequency (%)
C7
20.0%
T6
17.1%
S6
17.1%
B3
8.6%
R2
 
5.7%
N2
 
5.7%
M2
 
5.7%
F1
 
2.9%
W1
 
2.9%
L1
 
2.9%
Other values (4)4
11.4%
Other Punctuation
ValueCountFrequency (%)
/14
34.1%
.14
34.1%
,10
24.4%
'2
 
4.9%
@1
 
2.4%
Space Separator
ValueCountFrequency (%)
307
98.4%
 5
 
1.6%
Math Symbol
ValueCountFrequency (%)
>23
50.0%
<23
50.0%
Decimal Number
ValueCountFrequency (%)
22
50.0%
02
50.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1763
81.3%
Common405
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n217
12.3%
e200
11.3%
a183
10.4%
t140
 
7.9%
i117
 
6.6%
o113
 
6.4%
s103
 
5.8%
h96
 
5.4%
r89
 
5.0%
l63
 
3.6%
Other values (29)442
25.1%
Common
ValueCountFrequency (%)
307
75.8%
>23
 
5.7%
<23
 
5.7%
/14
 
3.5%
.14
 
3.5%
,10
 
2.5%
 5
 
1.2%
22
 
0.5%
'2
 
0.5%
02
 
0.5%
Other values (3)3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2163
99.8%
None5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
307
14.2%
n217
 
10.0%
e200
 
9.2%
a183
 
8.5%
t140
 
6.5%
i117
 
5.4%
o113
 
5.2%
s103
 
4.8%
h96
 
4.4%
r89
 
4.1%
Other values (41)598
27.6%
None
ValueCountFrequency (%)
 5
100.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct48
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48168.53425
Minimum2504
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:29.940961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2504
5-th percentile15250
Q149400
median52655
Q352933
95-th percentile56288
Maximum61755
Range59251
Interquartile range (IQR)3533

Descriptive statistics

Standard deviation11813.28077
Coefficient of variation (CV)0.2452489153
Kurtosis5.205040019
Mean48168.53425
Median Absolute Deviation (MAD)1378
Skewness-2.412166791
Sum3516303
Variance139553602.7
MonotonicityNot monotonic
2022-05-09T21:23:30.051007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
527367
 
9.6%
549556
 
8.2%
526554
 
5.5%
526614
 
5.5%
414902
 
2.7%
521042
 
2.7%
527842
 
2.7%
524002
 
2.7%
152502
 
2.7%
525242
 
2.7%
Other values (38)40
54.8%
ValueCountFrequency (%)
25041
1.4%
133811
1.4%
133921
1.4%
152502
2.7%
176331
1.4%
306061
1.4%
329801
1.4%
383391
1.4%
414902
2.7%
421211
1.4%
ValueCountFrequency (%)
617551
 
1.4%
586451
 
1.4%
573391
 
1.4%
562882
 
2.7%
550161
 
1.4%
550021
 
1.4%
549556
8.2%
540331
 
1.4%
536691
 
1.4%
534671
 
1.4%

_embedded_show_url
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size712.0 B
https://www.tvmaze.com/shows/52736/paurashpur
https://www.tvmaze.com/shows/54955/tillykke-i-skal-have-trillinger
https://www.tvmaze.com/shows/52655/the-case-solver
 
4
https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-story
 
4
https://www.tvmaze.com/shows/41490/unique-lady
 
2
Other values (43)
50 

Length

Max length71
Median length59
Mean length51.78082192
Min length39

Characters and Unicode

Total characters3780
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)49.3%

Sample

1st rowhttps://www.tvmaze.com/shows/51065/stand-up-autsajd
2nd rowhttps://www.tvmaze.com/shows/52044/blic-krik
3rd rowhttps://www.tvmaze.com/shows/52933/lab-s-antonom-belaevym
4th rowhttps://www.tvmaze.com/shows/51336/core-sense
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52736/paurashpur7
 
9.6%
https://www.tvmaze.com/shows/54955/tillykke-i-skal-have-trillinger6
 
8.2%
https://www.tvmaze.com/shows/52655/the-case-solver4
 
5.5%
https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-story4
 
5.5%
https://www.tvmaze.com/shows/41490/unique-lady2
 
2.7%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love2
 
2.7%
https://www.tvmaze.com/shows/52784/unique-lady-22
 
2.7%
https://www.tvmaze.com/shows/52400/dream-detective2
 
2.7%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.7%
https://www.tvmaze.com/shows/52524/forever-love2
 
2.7%
Other values (38)40
54.8%

Length

2022-05-09T21:23:30.160782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52736/paurashpur7
 
9.6%
https://www.tvmaze.com/shows/54955/tillykke-i-skal-have-trillinger6
 
8.2%
https://www.tvmaze.com/shows/52655/the-case-solver4
 
5.5%
https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-story4
 
5.5%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
2.7%
https://www.tvmaze.com/shows/56288/nieobecni2
 
2.7%
https://www.tvmaze.com/shows/52936/my-best-friends-story2
 
2.7%
https://www.tvmaze.com/shows/52524/forever-love2
 
2.7%
https://www.tvmaze.com/shows/52400/dream-detective2
 
2.7%
https://www.tvmaze.com/shows/52784/unique-lady-22
 
2.7%
Other values (38)40
54.8%

Most occurring characters

ValueCountFrequency (%)
/365
 
9.7%
t306
 
8.1%
w302
 
8.0%
s280
 
7.4%
e207
 
5.5%
o203
 
5.4%
h182
 
4.8%
m166
 
4.4%
a161
 
4.3%
.146
 
3.9%
Other values (30)1462
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2683
71.0%
Other Punctuation584
 
15.4%
Decimal Number375
 
9.9%
Dash Punctuation138
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t306
11.4%
w302
11.3%
s280
10.4%
e207
 
7.7%
o203
 
7.6%
h182
 
6.8%
m166
 
6.2%
a161
 
6.0%
c102
 
3.8%
v98
 
3.7%
Other values (16)676
25.2%
Decimal Number
ValueCountFrequency (%)
587
23.2%
255
14.7%
443
11.5%
638
10.1%
337
9.9%
128
 
7.5%
026
 
6.9%
923
 
6.1%
721
 
5.6%
817
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/365
62.5%
.146
 
25.0%
:73
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2683
71.0%
Common1097
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t306
11.4%
w302
11.3%
s280
10.4%
e207
 
7.7%
o203
 
7.6%
h182
 
6.8%
m166
 
6.2%
a161
 
6.0%
c102
 
3.8%
v98
 
3.7%
Other values (16)676
25.2%
Common
ValueCountFrequency (%)
/365
33.3%
.146
 
13.3%
-138
 
12.6%
587
 
7.9%
:73
 
6.7%
255
 
5.0%
443
 
3.9%
638
 
3.5%
337
 
3.4%
128
 
2.6%
Other values (4)87
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3780
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/365
 
9.7%
t306
 
8.1%
w302
 
8.0%
s280
 
7.4%
e207
 
5.5%
o203
 
5.4%
h182
 
4.8%
m166
 
4.4%
a161
 
4.3%
.146
 
3.9%
Other values (30)1462
38.7%

_embedded_show_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size712.0 B
Paurashpur
Tillykke, I skal have trillinger!
The Case Solver
 
4
Celebrity: A 21st-Century Story
 
4
Unique Lady
 
2
Other values (43)
50 

Length

Max length36
Median length25
Mean length17.10958904
Min length6

Characters and Unicode

Total characters1249
Distinct characters80
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)49.3%

Sample

1st rowStand Up Аутсайд
2nd rowБлиц-крик
3rd rowLAB с Антоном Беляевым
4th rowCore Sense
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
Paurashpur7
 
9.6%
Tillykke, I skal have trillinger!6
 
8.2%
The Case Solver4
 
5.5%
Celebrity: A 21st-Century Story4
 
5.5%
Unique Lady2
 
2.7%
Twisted Fate of Love2
 
2.7%
Unique Lady 22
 
2.7%
Dream Detective2
 
2.7%
The Young Turks2
 
2.7%
Forever Love2
 
2.7%
Other values (38)40
54.8%

Length

2022-05-09T21:23:30.270833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the10
 
4.9%
paurashpur7
 
3.4%
story6
 
2.9%
i6
 
2.9%
skal6
 
2.9%
have6
 
2.9%
trillinger6
 
2.9%
tillykke6
 
2.9%
of6
 
2.9%
love5
 
2.4%
Other values (101)142
68.9%

Most occurring characters

ValueCountFrequency (%)
133
 
10.6%
e129
 
10.3%
r74
 
5.9%
a69
 
5.5%
i68
 
5.4%
t58
 
4.6%
l54
 
4.3%
n53
 
4.2%
o53
 
4.2%
s42
 
3.4%
Other values (70)516
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter901
72.1%
Uppercase Letter176
 
14.1%
Space Separator133
 
10.6%
Other Punctuation23
 
1.8%
Decimal Number11
 
0.9%
Dash Punctuation5
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e129
14.3%
r74
 
8.2%
a69
 
7.7%
i68
 
7.5%
t58
 
6.4%
l54
 
6.0%
n53
 
5.9%
o53
 
5.9%
s42
 
4.7%
u41
 
4.6%
Other values (36)260
28.9%
Uppercase Letter
ValueCountFrequency (%)
T25
14.2%
S17
 
9.7%
C15
 
8.5%
L15
 
8.5%
A15
 
8.5%
F8
 
4.5%
N8
 
4.5%
P8
 
4.5%
B7
 
4.0%
I7
 
4.0%
Other values (15)51
29.0%
Other Punctuation
ValueCountFrequency (%)
,7
30.4%
!6
26.1%
'5
21.7%
:5
21.7%
Decimal Number
ValueCountFrequency (%)
26
54.5%
14
36.4%
71
 
9.1%
Space Separator
ValueCountFrequency (%)
133
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1032
82.6%
Common172
 
13.8%
Cyrillic45
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e129
 
12.5%
r74
 
7.2%
a69
 
6.7%
i68
 
6.6%
t58
 
5.6%
l54
 
5.2%
n53
 
5.1%
o53
 
5.1%
s42
 
4.1%
u41
 
4.0%
Other values (37)391
37.9%
Cyrillic
ValueCountFrequency (%)
т4
 
8.9%
и4
 
8.9%
о4
 
8.9%
р3
 
6.7%
к3
 
6.7%
л2
 
4.4%
е2
 
4.4%
Б2
 
4.4%
м2
 
4.4%
н2
 
4.4%
Other values (14)17
37.8%
Common
ValueCountFrequency (%)
133
77.3%
,7
 
4.1%
26
 
3.5%
!6
 
3.5%
'5
 
2.9%
:5
 
2.9%
-5
 
2.9%
14
 
2.3%
71
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1204
96.4%
Cyrillic45
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
133
 
11.0%
e129
 
10.7%
r74
 
6.1%
a69
 
5.7%
i68
 
5.6%
t58
 
4.8%
l54
 
4.5%
n53
 
4.4%
o53
 
4.4%
s42
 
3.5%
Other values (46)471
39.1%
Cyrillic
ValueCountFrequency (%)
т4
 
8.9%
и4
 
8.9%
о4
 
8.9%
р3
 
6.7%
к3
 
6.7%
л2
 
4.4%
е2
 
4.4%
Б2
 
4.4%
м2
 
4.4%
н2
 
4.4%
Other values (14)17
37.8%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size712.0 B
Scripted
39 
Documentary
14 
Animation
Reality
Variety
 
3
Other values (4)

Length

Max length11
Median length8
Mean length8.328767123
Min length4

Characters and Unicode

Total characters608
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowVariety
2nd rowVariety
3rd rowDocumentary
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted39
53.4%
Documentary14
 
19.2%
Animation4
 
5.5%
Reality4
 
5.5%
Variety3
 
4.1%
News3
 
4.1%
Sports3
 
4.1%
Talk Show2
 
2.7%
Game Show1
 
1.4%

Length

2022-05-09T21:23:30.380480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:30.516014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted39
51.3%
documentary14
 
18.4%
animation4
 
5.3%
reality4
 
5.3%
variety3
 
3.9%
news3
 
3.9%
sports3
 
3.9%
show3
 
3.9%
talk2
 
2.6%
game1
 
1.3%

Most occurring characters

ValueCountFrequency (%)
t67
11.0%
e64
10.5%
r59
9.7%
i54
8.9%
c53
 
8.7%
S45
 
7.4%
p42
 
6.9%
d39
 
6.4%
a28
 
4.6%
o24
 
3.9%
Other values (17)133
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter529
87.0%
Uppercase Letter76
 
12.5%
Space Separator3
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t67
12.7%
e64
12.1%
r59
11.2%
i54
10.2%
c53
10.0%
p42
7.9%
d39
7.4%
a28
 
5.3%
o24
 
4.5%
n22
 
4.2%
Other values (8)77
14.6%
Uppercase Letter
ValueCountFrequency (%)
S45
59.2%
D14
 
18.4%
A4
 
5.3%
R4
 
5.3%
V3
 
3.9%
N3
 
3.9%
T2
 
2.6%
G1
 
1.3%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin605
99.5%
Common3
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t67
11.1%
e64
10.6%
r59
9.8%
i54
8.9%
c53
8.8%
S45
 
7.4%
p42
 
6.9%
d39
 
6.4%
a28
 
4.6%
o24
 
4.0%
Other values (16)130
21.5%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t67
11.0%
e64
10.5%
r59
9.7%
i54
8.9%
c53
 
8.7%
S45
 
7.4%
p42
 
6.9%
d39
 
6.4%
a28
 
4.6%
o24
 
3.9%
Other values (17)133
21.9%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size712.0 B
Chinese
21 
English
17 
Hindi
Danish
Korean
Other values (10)
18 

Length

Max length10
Median length7
Mean length6.671232877
Min length4

Characters and Unicode

Total characters487
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.8%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese21
28.8%
English17
23.3%
Hindi7
 
9.6%
Danish6
 
8.2%
Korean4
 
5.5%
Norwegian4
 
5.5%
Russian3
 
4.1%
Polish2
 
2.7%
Thai2
 
2.7%
Arabic2
 
2.7%
Other values (5)5
 
6.8%

Length

2022-05-09T21:23:30.642010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese21
28.8%
english17
23.3%
hindi7
 
9.6%
danish6
 
8.2%
korean4
 
5.5%
norwegian4
 
5.5%
russian3
 
4.1%
polish2
 
2.7%
thai2
 
2.7%
arabic2
 
2.7%
Other values (5)5
 
6.8%

Most occurring characters

ValueCountFrequency (%)
i75
15.4%
n66
13.6%
e56
11.5%
s54
11.1%
h49
10.1%
a25
 
5.1%
C21
 
4.3%
g21
 
4.3%
l19
 
3.9%
E17
 
3.5%
Other values (20)84
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter414
85.0%
Uppercase Letter73
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i75
18.1%
n66
15.9%
e56
13.5%
s54
13.0%
h49
11.8%
a25
 
6.0%
g21
 
5.1%
l19
 
4.6%
r13
 
3.1%
o10
 
2.4%
Other values (8)26
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
C21
28.8%
E17
23.3%
H8
 
11.0%
D7
 
9.6%
K4
 
5.5%
N4
 
5.5%
P3
 
4.1%
R3
 
4.1%
A2
 
2.7%
T2
 
2.7%
Other values (2)2
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin487
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i75
15.4%
n66
13.6%
e56
11.5%
s54
11.1%
h49
10.1%
a25
 
5.1%
C21
 
4.3%
g21
 
4.3%
l19
 
3.9%
E17
 
3.5%
Other values (20)84
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII487
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i75
15.4%
n66
13.6%
e56
11.5%
s54
11.1%
h49
10.1%
a25
 
5.1%
C21
 
4.3%
g21
 
4.3%
l19
 
3.9%
E17
 
3.5%
Other values (20)84
17.2%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size712.0 B
[]
15 
['Drama']
['Drama', 'Romance']
['Children', 'Family']
['Crime']
Other values (19)
30 

Length

Max length45
Median length38
Mean length15.45205479
Min length2

Characters and Unicode

Total characters1128
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)17.8%

Sample

1st row[]
2nd row['Comedy']
3rd row['Music']
4th row['Action', 'Anime', 'Science-Fiction']
5th row['Action', 'Adventure', 'Anime', 'Fantasy']

Common Values

ValueCountFrequency (%)
[]15
20.5%
['Drama']9
12.3%
['Drama', 'Romance']9
12.3%
['Children', 'Family']6
 
8.2%
['Crime']4
 
5.5%
['Comedy']4
 
5.5%
['History']4
 
5.5%
['Drama', 'Romance', 'History']3
 
4.1%
['Drama', 'Fantasy', 'Mystery']2
 
2.7%
['Drama', 'Comedy', 'Romance']2
 
2.7%
Other values (14)15
20.5%

Length

2022-05-09T21:23:30.830229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama27
22.1%
romance18
14.8%
15
12.3%
comedy11
9.0%
history7
 
5.7%
children7
 
5.7%
family7
 
5.7%
fantasy6
 
4.9%
crime5
 
4.1%
action4
 
3.3%
Other values (7)15
12.3%

Most occurring characters

ValueCountFrequency (%)
'214
19.0%
a92
 
8.2%
[73
 
6.5%
]73
 
6.5%
m72
 
6.4%
e56
 
5.0%
r52
 
4.6%
,49
 
4.3%
49
 
4.3%
o46
 
4.1%
Other values (21)352
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter559
49.6%
Other Punctuation263
23.3%
Uppercase Letter109
 
9.7%
Open Punctuation73
 
6.5%
Close Punctuation73
 
6.5%
Space Separator49
 
4.3%
Dash Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a92
16.5%
m72
12.9%
e56
10.0%
r52
9.3%
o46
8.2%
n45
8.1%
i41
7.3%
y37
6.6%
c29
 
5.2%
t24
 
4.3%
Other values (6)65
11.6%
Uppercase Letter
ValueCountFrequency (%)
D27
24.8%
C23
21.1%
R18
16.5%
F17
15.6%
A10
 
9.2%
H7
 
6.4%
M4
 
3.7%
S2
 
1.8%
W1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
'214
81.4%
,49
 
18.6%
Open Punctuation
ValueCountFrequency (%)
[73
100.0%
Close Punctuation
ValueCountFrequency (%)
]73
100.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin668
59.2%
Common460
40.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a92
13.8%
m72
10.8%
e56
 
8.4%
r52
 
7.8%
o46
 
6.9%
n45
 
6.7%
i41
 
6.1%
y37
 
5.5%
c29
 
4.3%
D27
 
4.0%
Other values (15)171
25.6%
Common
ValueCountFrequency (%)
'214
46.5%
[73
 
15.9%
]73
 
15.9%
,49
 
10.7%
49
 
10.7%
-2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'214
19.0%
a92
 
8.2%
[73
 
6.5%
]73
 
6.5%
m72
 
6.4%
e56
 
5.0%
r52
 
4.6%
,49
 
4.3%
49
 
4.3%
o46
 
4.1%
Other values (21)352
31.2%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size712.0 B
Running
35 
Ended
26 
To Be Determined
12 

Length

Max length16
Median length7
Mean length7.767123288
Min length5

Characters and Unicode

Total characters567
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowRunning
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running35
47.9%
Ended26
35.6%
To Be Determined12
 
16.4%

Length

2022-05-09T21:23:30.924690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:31.018548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running35
36.1%
ended26
26.8%
to12
 
12.4%
be12
 
12.4%
determined12
 
12.4%

Most occurring characters

ValueCountFrequency (%)
n143
25.2%
e74
13.1%
d64
11.3%
i47
 
8.3%
R35
 
6.2%
u35
 
6.2%
g35
 
6.2%
E26
 
4.6%
24
 
4.2%
T12
 
2.1%
Other values (6)72
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter446
78.7%
Uppercase Letter97
 
17.1%
Space Separator24
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n143
32.1%
e74
16.6%
d64
14.3%
i47
 
10.5%
u35
 
7.8%
g35
 
7.8%
o12
 
2.7%
t12
 
2.7%
r12
 
2.7%
m12
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
R35
36.1%
E26
26.8%
T12
 
12.4%
B12
 
12.4%
D12
 
12.4%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin543
95.8%
Common24
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n143
26.3%
e74
13.6%
d64
11.8%
i47
 
8.7%
R35
 
6.4%
u35
 
6.4%
g35
 
6.4%
E26
 
4.8%
T12
 
2.2%
o12
 
2.2%
Other values (5)60
11.0%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n143
25.2%
e74
13.1%
d64
11.3%
i47
 
8.3%
R35
 
6.2%
u35
 
6.2%
g35
 
6.2%
E26
 
4.6%
24
 
4.2%
T12
 
2.1%
Other values (6)72
12.7%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)34.0%
Missing20
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean42.28301887
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:31.096828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.2
Q127
median38
Q345
95-th percentile102
Maximum120
Range116
Interquartile range (IQR)18

Descriptive statistics

Standard deviation27.0337235
Coefficient of variation (CV)0.6393517828
Kurtosis2.270625718
Mean42.28301887
Median Absolute Deviation (MAD)11
Skewness1.46967552
Sum2241
Variance730.8222061
MonotonicityNot monotonic
2022-05-09T21:23:31.190502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4513
17.8%
309
12.3%
605
 
6.8%
274
 
5.5%
1203
 
4.1%
903
 
4.1%
402
 
2.7%
382
 
2.7%
152
 
2.7%
252
 
2.7%
Other values (8)8
 
11.0%
(Missing)20
27.4%
ValueCountFrequency (%)
41
1.4%
51
1.4%
81
1.4%
101
1.4%
121
1.4%
152
2.7%
231
1.4%
241
1.4%
252
2.7%
261
1.4%
ValueCountFrequency (%)
1203
 
4.1%
903
 
4.1%
605
 
6.8%
4513
17.8%
402
 
2.7%
382
 
2.7%
309
12.3%
274
 
5.5%
261
 
1.4%
252
 
2.7%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)36.6%
Missing2
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean37.69014085
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:31.269316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.5
Q120
median30
Q345
95-th percentile90
Maximum120
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation26.13240547
Coefficient of variation (CV)0.6933485756
Kurtosis2.735552589
Mean37.69014085
Median Absolute Deviation (MAD)13
Skewness1.613647131
Sum2676
Variance682.9026157
MonotonicityNot monotonic
2022-05-09T21:23:31.379459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4512
16.4%
308
11.0%
207
 
9.6%
275
 
6.8%
605
 
6.8%
424
 
5.5%
1203
 
4.1%
903
 
4.1%
253
 
4.1%
123
 
4.1%
Other values (16)18
24.7%
ValueCountFrequency (%)
41
 
1.4%
51
 
1.4%
61
 
1.4%
81
 
1.4%
91
 
1.4%
101
 
1.4%
123
4.1%
152
 
2.7%
171
 
1.4%
207
9.6%
ValueCountFrequency (%)
1203
 
4.1%
903
 
4.1%
871
 
1.4%
605
6.8%
4512
16.4%
424
 
5.5%
401
 
1.4%
361
 
1.4%
321
 
1.4%
308
11.0%

_embedded_show_premiered
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct39
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Memory size712.0 B
2020-12-29
14 
2019-08-22
2020-12-21
2020-12-08
 
3
2020-10-13
 
2
Other values (34)
43 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters730
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)34.2%

Sample

1st row2020-10-13
2nd row2019-08-20
3rd row2019-12-17
4th row2020-10-13
5th row2020-03-08

Common Values

ValueCountFrequency (%)
2020-12-2914
19.2%
2019-08-226
 
8.2%
2020-12-215
 
6.8%
2020-12-083
 
4.1%
2020-10-132
 
2.7%
2020-12-272
 
2.7%
2020-12-282
 
2.7%
2020-12-142
 
2.7%
2020-11-232
 
2.7%
2020-12-222
 
2.7%
Other values (29)33
45.2%

Length

2022-05-09T21:23:31.473560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2914
19.2%
2019-08-226
 
8.2%
2020-12-215
 
6.8%
2020-12-083
 
4.1%
2020-12-222
 
2.7%
2019-10-082
 
2.7%
2020-12-242
 
2.7%
2013-12-242
 
2.7%
2019-01-172
 
2.7%
2020-11-232
 
2.7%
Other values (29)33
45.2%

Most occurring characters

ValueCountFrequency (%)
2214
29.3%
0162
22.2%
-146
20.0%
1112
15.3%
938
 
5.2%
822
 
3.0%
312
 
1.6%
710
 
1.4%
48
 
1.1%
56
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number584
80.0%
Dash Punctuation146
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2214
36.6%
0162
27.7%
1112
19.2%
938
 
6.5%
822
 
3.8%
312
 
2.1%
710
 
1.7%
48
 
1.4%
56
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2214
29.3%
0162
22.2%
-146
20.0%
1112
15.3%
938
 
5.2%
822
 
3.0%
312
 
1.6%
710
 
1.4%
48
 
1.1%
56
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2214
29.3%
0162
22.2%
-146
20.0%
1112
15.3%
938
 
5.2%
822
 
3.0%
312
 
1.6%
710
 
1.4%
48
 
1.1%
56
 
0.8%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size712.0 B
nan
47 
2020-12-29
2021-01-05
2020-12-30
 
3
2020-12-31
 
2
Other values (6)

Length

Max length10
Median length3
Mean length5.493150685
Min length3

Characters and Unicode

Total characters401
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.8%

Sample

1st row2020-12-31
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan47
64.4%
2020-12-299
 
12.3%
2021-01-055
 
6.8%
2020-12-303
 
4.1%
2020-12-312
 
2.7%
2021-01-072
 
2.7%
2021-01-201
 
1.4%
2021-01-061
 
1.4%
2021-02-161
 
1.4%
2021-02-191
 
1.4%

Length

2022-05-09T21:23:31.567734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan47
64.4%
2020-12-299
 
12.3%
2021-01-055
 
6.8%
2020-12-303
 
4.1%
2020-12-312
 
2.7%
2021-01-072
 
2.7%
2021-01-201
 
1.4%
2021-01-061
 
1.4%
2021-02-161
 
1.4%
2021-02-191
 
1.4%

Most occurring characters

ValueCountFrequency (%)
n94
23.4%
278
19.5%
065
16.2%
-52
13.0%
a47
11.7%
139
9.7%
911
 
2.7%
36
 
1.5%
55
 
1.2%
72
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number208
51.9%
Lowercase Letter141
35.2%
Dash Punctuation52
 
13.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
278
37.5%
065
31.2%
139
18.8%
911
 
5.3%
36
 
2.9%
55
 
2.4%
72
 
1.0%
62
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
n94
66.7%
a47
33.3%
Dash Punctuation
ValueCountFrequency (%)
-52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common260
64.8%
Latin141
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
278
30.0%
065
25.0%
-52
20.0%
139
15.0%
911
 
4.2%
36
 
2.3%
55
 
1.9%
72
 
0.8%
62
 
0.8%
Latin
ValueCountFrequency (%)
n94
66.7%
a47
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n94
23.4%
278
19.5%
065
16.2%
-52
13.0%
a47
11.7%
139
9.7%
911
 
2.7%
36
 
1.5%
55
 
1.2%
72
 
0.5%

_embedded_show_officialSite
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size712.0 B
nan
11 
https://www.altbalaji.com/show/paurashpur/347
https://www.dr.dk/drtv/episode/tillykke-i-skal-have-trillinger_130555
https://www.bbc.co.uk/programmes/m000qsk1
 
4
https://www.iqiyi.com/a_c4m3iuc94t.html
 
4
Other values (36)
41 

Length

Max length86
Median length63
Mean length42.02739726
Min length3

Characters and Unicode

Total characters3068
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)42.5%

Sample

1st rowhttps://premier.one/show/13734
2nd rowhttps://www.youtube.com/channel/UCrMU71nSWHAmpb1-3wEtW8g/videos
3rd rowhttps://hd.kinopoisk.ru/film/41a971dd517a30f9b86d20def219c326
4th rowhttps://www.bilibili.com/bangumi/media/md28223064
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
nan11
 
15.1%
https://www.altbalaji.com/show/paurashpur/3477
 
9.6%
https://www.dr.dk/drtv/episode/tillykke-i-skal-have-trillinger_1305556
 
8.2%
https://www.bbc.co.uk/programmes/m000qsk14
 
5.5%
https://www.iqiyi.com/a_c4m3iuc94t.html4
 
5.5%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=2
 
2.7%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
2.7%
http://www.iqiyi.com/a_19rrhvpyyp.html2
 
2.7%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
2.7%
https://www.tytnetwork.com2
 
2.7%
Other values (31)31
42.5%

Length

2022-05-09T21:23:31.698556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan11
 
15.1%
https://www.altbalaji.com/show/paurashpur/3477
 
9.6%
https://www.dr.dk/drtv/episode/tillykke-i-skal-have-trillinger_1305556
 
8.2%
https://www.bbc.co.uk/programmes/m000qsk14
 
5.5%
https://www.iqiyi.com/a_c4m3iuc94t.html4
 
5.5%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab2
 
2.7%
https://v.qq.com/detail/m/mzc00200ur8p8zp.html2
 
2.7%
http://www.iqiyi.com/a_19rrhvpyyp.html2
 
2.7%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html2
 
2.7%
https://www.tytnetwork.com2
 
2.7%
Other values (31)31
42.5%

Most occurring characters

ValueCountFrequency (%)
/262
 
8.5%
t237
 
7.7%
s147
 
4.8%
.137
 
4.5%
w136
 
4.4%
a130
 
4.2%
i121
 
3.9%
h119
 
3.9%
e114
 
3.7%
o113
 
3.7%
Other values (64)1552
50.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2079
67.8%
Other Punctuation501
 
16.3%
Decimal Number292
 
9.5%
Uppercase Letter119
 
3.9%
Dash Punctuation45
 
1.5%
Connector Punctuation19
 
0.6%
Math Symbol13
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t237
 
11.4%
s147
 
7.1%
w136
 
6.5%
a130
 
6.3%
i121
 
5.8%
h119
 
5.7%
e114
 
5.5%
o113
 
5.4%
p112
 
5.4%
l106
 
5.1%
Other values (16)744
35.8%
Uppercase Letter
ValueCountFrequency (%)
E14
 
11.8%
A11
 
9.2%
W10
 
8.4%
L8
 
6.7%
B8
 
6.7%
P7
 
5.9%
F7
 
5.9%
C7
 
5.9%
R5
 
4.2%
D5
 
4.2%
Other values (15)37
31.1%
Decimal Number
ValueCountFrequency (%)
045
15.4%
438
13.0%
538
13.0%
134
11.6%
333
11.3%
226
8.9%
922
7.5%
721
7.2%
820
6.8%
615
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/262
52.3%
.137
27.3%
:62
 
12.4%
%25
 
5.0%
?7
 
1.4%
&4
 
0.8%
'2
 
0.4%
#1
 
0.2%
!1
 
0.2%
Math Symbol
ValueCountFrequency (%)
=11
84.6%
+2
 
15.4%
Dash Punctuation
ValueCountFrequency (%)
-45
100.0%
Connector Punctuation
ValueCountFrequency (%)
_19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2198
71.6%
Common870
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t237
 
10.8%
s147
 
6.7%
w136
 
6.2%
a130
 
5.9%
i121
 
5.5%
h119
 
5.4%
e114
 
5.2%
o113
 
5.1%
p112
 
5.1%
l106
 
4.8%
Other values (41)863
39.3%
Common
ValueCountFrequency (%)
/262
30.1%
.137
15.7%
:62
 
7.1%
-45
 
5.2%
045
 
5.2%
438
 
4.4%
538
 
4.4%
134
 
3.9%
333
 
3.8%
226
 
3.0%
Other values (13)150
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/262
 
8.5%
t237
 
7.7%
s147
 
4.8%
.137
 
4.5%
w136
 
4.4%
a130
 
4.2%
i121
 
3.9%
h119
 
3.9%
e114
 
3.7%
o113
 
3.7%
Other values (64)1552
50.6%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct37
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.52054795
Minimum2
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:31.792517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.8
Q117
median25
Q335
95-th percentile76.4
Maximum91
Range89
Interquartile range (IQR)18

Descriptive statistics

Standard deviation21.59803848
Coefficient of variation (CV)0.7316272897
Kurtosis0.6218452034
Mean29.52054795
Median Absolute Deviation (MAD)9
Skewness1.138823923
Sum2155
Variance466.4752664
MonotonicityNot monotonic
2022-05-09T21:23:31.886615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1810
 
13.7%
67
 
9.6%
284
 
5.5%
344
 
5.5%
303
 
4.1%
273
 
4.1%
33
 
4.1%
682
 
2.7%
142
 
2.7%
192
 
2.7%
Other values (27)33
45.2%
ValueCountFrequency (%)
21
 
1.4%
33
 
4.1%
67
9.6%
81
 
1.4%
101
 
1.4%
142
 
2.7%
152
 
2.7%
172
 
2.7%
1810
13.7%
192
 
2.7%
ValueCountFrequency (%)
911
1.4%
821
1.4%
791
1.4%
771
1.4%
761
1.4%
682
2.7%
671
1.4%
661
1.4%
651
1.4%
621
1.4%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size712.0 B
nan
72 
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}
 
1

Length

Max length66
Median length3
Mean length3.863013699
Min length3

Characters and Unicode

Total characters282
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan72
98.6%
{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}1
 
1.4%

Length

2022-05-09T21:23:31.996500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:23:32.075196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan72
92.3%
name1
 
1.3%
ukraine1
 
1.3%
code1
 
1.3%
ua1
 
1.3%
timezone1
 
1.3%
europe/zaporozhye1
 
1.3%

Most occurring characters

ValueCountFrequency (%)
n147
52.1%
a75
26.6%
'12
 
4.3%
e7
 
2.5%
o5
 
1.8%
5
 
1.8%
:3
 
1.1%
r3
 
1.1%
i2
 
0.7%
p2
 
0.7%
Other values (17)21
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter252
89.4%
Other Punctuation18
 
6.4%
Space Separator5
 
1.8%
Uppercase Letter5
 
1.8%
Open Punctuation1
 
0.4%
Close Punctuation1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n147
58.3%
a75
29.8%
e7
 
2.8%
o5
 
2.0%
r3
 
1.2%
i2
 
0.8%
p2
 
0.8%
z2
 
0.8%
m2
 
0.8%
u1
 
0.4%
Other values (6)6
 
2.4%
Other Punctuation
ValueCountFrequency (%)
'12
66.7%
:3
 
16.7%
,2
 
11.1%
/1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
U2
40.0%
Z1
20.0%
E1
20.0%
A1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
{1
100.0%
Close Punctuation
ValueCountFrequency (%)
}1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin257
91.1%
Common25
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n147
57.2%
a75
29.2%
e7
 
2.7%
o5
 
1.9%
r3
 
1.2%
i2
 
0.8%
p2
 
0.8%
z2
 
0.8%
U2
 
0.8%
m2
 
0.8%
Other values (10)10
 
3.9%
Common
ValueCountFrequency (%)
'12
48.0%
5
20.0%
:3
 
12.0%
,2
 
8.0%
/1
 
4.0%
{1
 
4.0%
}1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n147
52.1%
a75
26.6%
'12
 
4.3%
e7
 
2.5%
o5
 
1.8%
5
 
1.8%
:3
 
1.1%
r3
 
1.1%
i2
 
0.7%
p2
 
0.7%
Other values (17)21
 
7.4%

_embedded_show_summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)61.6%
Missing0
Missing (%)0.0%
Memory size712.0 B
<p>In a kingdom dominated by men, it is the ultimate battle of the sexes to win the war being waged against gender equality. Every character is grey and they must fight for what they believe is right in order to survive in Paurashpur.</p>
<p>Becoming parents to triplets is a big life change and a marathon of a parenting task, which puts both family and parents under pressure.</p>
nan
 
4
<p>The play is set in the turbulent period of the Republic of China in Shanghai. In a turbulent era, the forensic doctor Che Suwei and the gentleman detective Gu Yuan are intertwined with various forces. "Deputy Inspector Kang Yichen, and the innocent and lively reporter Cao Qingluo worked together to crack out a number of weird and curious cases, and restore the truth.</p>
 
4
<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>
 
4
Other values (40)
48 

Length

Max length855
Median length437
Mean length297.369863
Min length3

Characters and Unicode

Total characters21708
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)43.8%

Sample

1st row<p>Solo performances of stand-up comedians from the underground and popular TV and Internet projects. Each new release is a new concert with its own atmosphere and humor.</p>
2nd row<p>A humorous show where participants ' knowledge is as important as humor! The game consists of three rounds. The host reads out the beginning of a quote, poem, or fact, and the contestant must finish them either funny or correctly! The winner is the one who turns out to be the most intelligent and fun!</p>
3rd row<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>
4th row<p>The power of beginnings, the energy of the core stone; one may find it good, one may find it evil. During a normal investigation, Yue Juntian finds himself drawn into the battle between the 'beginnings' of Yun City; Jiang Xin arrives in Yun City to stop Li Zunyuan's plan to take over. The two influence each other - one solves the mystery of their birth, the other redeems themselves. Together, they oppose Li Zunyuan.<br /> </p>
5th row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>

Common Values

ValueCountFrequency (%)
<p>In a kingdom dominated by men, it is the ultimate battle of the sexes to win the war being waged against gender equality. Every character is grey and they must fight for what they believe is right in order to survive in Paurashpur.</p>7
 
9.6%
<p>Becoming parents to triplets is a big life change and a marathon of a parenting task, which puts both family and parents under pressure.</p>6
 
8.2%
nan4
 
5.5%
<p>The play is set in the turbulent period of the Republic of China in Shanghai. In a turbulent era, the forensic doctor Che Suwei and the gentleman detective Gu Yuan are intertwined with various forces. "Deputy Inspector Kang Yichen, and the innocent and lively reporter Cao Qingluo worked together to crack out a number of weird and curious cases, and restore the truth.</p>4
 
5.5%
<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>4
 
5.5%
<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>2
 
2.7%
<p>Lin Luojing enters the XR system due to a technology competition, and time-travels to the Sheng Yuan Dynasty of the game. To return back to reality, she has to find her true love and max the "favorability points". In the midst of exchanging tactics with arrogant prince Zhong Wu Mei, her former personal guard Liu Xiu Wen returns to the capital, this time with a new identity as the Persian Prince. Liu Xiu Wen vows to wage war on Zhong Wuyan. Facing both internal and external crises and conflicts, how will Lin Luojing resolve it and embark on her journey back home?</p>2
 
2.7%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>2
 
2.7%
<p>Two unlikely individuals join forces to find the truth behind a series of murders using an unconventional method. Chen Si, a female detective with a sense of justice, unexpectedly becomess partners with Yuan Shuai, a dream interpreter with a dark past.</p>2
 
2.7%
<p>Set in the 20th century Shanghai, it revolves around two women with different backgrounds and personalities who forge a deep friendship as they support each other through hard times in life. Zhu Suo Suo is born into poverty and is taken in by Jiang Nan Sun's family. From then on, the two women became close friends. After they stepped out into society, Zhu Suo Suo quickly made a name for herself in the workforce with her talents and exemplary performance. Jiang Nan Sun continues to pursue academics, earning a reputation as an intellect. However, the Jiang family later met with trouble and fell into despair. Zhu Suo Suo helps Jiang Nan Sun settle into the workforce, by assisting her with living accommodations and work opportunities. With her hard work and knowledge, Jiang Nan Sun gradually transforms into an outstanding white-collar lady.</p>2
 
2.7%
Other values (35)38
52.1%

Length

2022-05-09T21:23:32.184721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the232
 
6.4%
and143
 
3.9%
of113
 
3.1%
to105
 
2.9%
a103
 
2.8%
in86
 
2.4%
is56
 
1.5%
with47
 
1.3%
her38
 
1.0%
they26
 
0.7%
Other values (1076)2676
73.8%

Most occurring characters

ValueCountFrequency (%)
3544
16.3%
e2125
 
9.8%
t1499
 
6.9%
n1330
 
6.1%
a1278
 
5.9%
i1206
 
5.6%
o1146
 
5.3%
r1121
 
5.2%
s963
 
4.4%
h864
 
4.0%
Other values (71)6632
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16544
76.2%
Space Separator3552
 
16.4%
Uppercase Letter620
 
2.9%
Other Punctuation534
 
2.5%
Math Symbol394
 
1.8%
Dash Punctuation38
 
0.2%
Decimal Number22
 
0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2125
12.8%
t1499
 
9.1%
n1330
 
8.0%
a1278
 
7.7%
i1206
 
7.3%
o1146
 
6.9%
r1121
 
6.8%
s963
 
5.8%
h864
 
5.2%
l593
 
3.6%
Other values (18)4419
26.7%
Uppercase Letter
ValueCountFrequency (%)
S69
 
11.1%
T69
 
11.1%
A38
 
6.1%
Y37
 
6.0%
L35
 
5.6%
W33
 
5.3%
C30
 
4.8%
I30
 
4.8%
D29
 
4.7%
H25
 
4.0%
Other values (16)225
36.3%
Other Punctuation
ValueCountFrequency (%)
,191
35.8%
.177
33.1%
/101
18.9%
'30
 
5.6%
"12
 
2.2%
!11
 
2.1%
:6
 
1.1%
?3
 
0.6%
;2
 
0.4%
1
 
0.2%
Decimal Number
ValueCountFrequency (%)
26
27.3%
04
18.2%
14
18.2%
53
13.6%
72
 
9.1%
31
 
4.5%
81
 
4.5%
41
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
-30
78.9%
7
 
18.4%
1
 
2.6%
Space Separator
ValueCountFrequency (%)
3544
99.8%
 8
 
0.2%
Math Symbol
ValueCountFrequency (%)
<197
50.0%
>197
50.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17164
79.1%
Common4544
 
20.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2125
12.4%
t1499
 
8.7%
n1330
 
7.7%
a1278
 
7.4%
i1206
 
7.0%
o1146
 
6.7%
r1121
 
6.5%
s963
 
5.6%
h864
 
5.0%
l593
 
3.5%
Other values (44)5039
29.4%
Common
ValueCountFrequency (%)
3544
78.0%
<197
 
4.3%
>197
 
4.3%
,191
 
4.2%
.177
 
3.9%
/101
 
2.2%
'30
 
0.7%
-30
 
0.7%
"12
 
0.3%
!11
 
0.2%
Other values (17)54
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII21688
99.9%
None11
 
0.1%
Punctuation9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3544
16.3%
e2125
 
9.8%
t1499
 
6.9%
n1330
 
6.1%
a1278
 
5.9%
i1206
 
5.6%
o1146
 
5.3%
r1121
 
5.2%
s963
 
4.4%
h864
 
4.0%
Other values (65)6612
30.5%
None
ValueCountFrequency (%)
 8
72.7%
ó2
 
18.2%
å1
 
9.1%
Punctuation
ValueCountFrequency (%)
7
77.8%
1
 
11.1%
1
 
11.1%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct48
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1626301833
Minimum1604587119
Maximum1651863266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size712.0 B
2022-05-09T21:23:32.294273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1604587119
5-th percentile1608833432
Q11609800105
median1619375021
Q31645412135
95-th percentile1651600840
Maximum1651863266
Range47276147
Interquartile range (IQR)35612030

Descriptive statistics

Standard deviation17507557.96
Coefficient of variation (CV)0.01076525747
Kurtosis-1.650362256
Mean1626301833
Median Absolute Deviation (MAD)9839880
Skewness0.4095542399
Sum1.187200338 × 1011
Variance3.065145856 × 1014
MonotonicityNot monotonic
2022-05-09T21:23:32.404119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
16098001057
 
9.6%
16193750216
 
8.2%
16091182014
 
5.5%
16113552204
 
5.5%
16518632662
 
2.7%
16095351412
 
2.7%
16096516762
 
2.7%
16128425832
 
2.7%
16481900582
 
2.7%
16124781452
 
2.7%
Other values (38)40
54.8%
ValueCountFrequency (%)
16045871191
 
1.4%
16078871751
 
1.4%
16083529671
 
1.4%
16084062791
 
1.4%
16091182014
5.5%
16095351412
 
2.7%
16096516762
 
2.7%
16098001057
9.6%
16099236481
 
1.4%
16105066872
 
2.7%
ValueCountFrequency (%)
16518632662
2.7%
16518386471
1.4%
16516154151
1.4%
16515911241
1.4%
16512614161
1.4%
16512534591
1.4%
16512532501
1.4%
16509088001
1.4%
16507501291
1.4%
16503120761
1.4%

_links_self_href
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size712.0 B
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2087588
 
1
https://api.tvmaze.com/episodes/2092729
 
1
https://api.tvmaze.com/episodes/1996798
 
1
https://api.tvmaze.com/episodes/2017664
 
1
Other values (68)
68 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters2847
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.4%
https://api.tvmaze.com/episodes/20875881
 
1.4%
https://api.tvmaze.com/episodes/20927291
 
1.4%
https://api.tvmaze.com/episodes/19967981
 
1.4%
https://api.tvmaze.com/episodes/20176641
 
1.4%
https://api.tvmaze.com/episodes/19852511
 
1.4%
https://api.tvmaze.com/episodes/19968201
 
1.4%
https://api.tvmaze.com/episodes/20682521
 
1.4%
https://api.tvmaze.com/episodes/20375321
 
1.4%
https://api.tvmaze.com/episodes/19939401
 
1.4%
Other values (63)63
86.3%

Length

2022-05-09T21:23:32.525215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
1.4%
https://api.tvmaze.com/episodes/20158181
 
1.4%
https://api.tvmaze.com/episodes/19640001
 
1.4%
https://api.tvmaze.com/episodes/19954051
 
1.4%
https://api.tvmaze.com/episodes/20077601
 
1.4%
https://api.tvmaze.com/episodes/19857891
 
1.4%
https://api.tvmaze.com/episodes/20396221
 
1.4%
https://api.tvmaze.com/episodes/20396231
 
1.4%
https://api.tvmaze.com/episodes/23244271
 
1.4%
https://api.tvmaze.com/episodes/23244281
 
1.4%
Other values (63)63
86.3%

Most occurring characters

ValueCountFrequency (%)
/292
 
10.3%
p219
 
7.7%
s219
 
7.7%
e219
 
7.7%
t219
 
7.7%
o146
 
5.1%
a146
 
5.1%
i146
 
5.1%
.146
 
5.1%
m146
 
5.1%
Other values (16)949
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1825
64.1%
Other Punctuation511
 
17.9%
Decimal Number511
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p219
12.0%
s219
12.0%
e219
12.0%
t219
12.0%
o146
8.0%
a146
8.0%
i146
8.0%
m146
8.0%
h73
 
4.0%
d73
 
4.0%
Other values (3)219
12.0%
Decimal Number
ValueCountFrequency (%)
287
17.0%
987
17.0%
168
13.3%
047
9.2%
847
9.2%
341
8.0%
637
7.2%
735
6.8%
433
 
6.5%
529
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/292
57.1%
.146
28.6%
:73
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1825
64.1%
Common1022
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/292
28.6%
.146
14.3%
287
 
8.5%
987
 
8.5%
:73
 
7.1%
168
 
6.7%
047
 
4.6%
847
 
4.6%
341
 
4.0%
637
 
3.6%
Other values (3)97
 
9.5%
Latin
ValueCountFrequency (%)
p219
12.0%
s219
12.0%
e219
12.0%
t219
12.0%
o146
8.0%
a146
8.0%
i146
8.0%
m146
8.0%
h73
 
4.0%
d73
 
4.0%
Other values (3)219
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/292
 
10.3%
p219
 
7.7%
s219
 
7.7%
e219
 
7.7%
t219
 
7.7%
o146
 
5.1%
a146
 
5.1%
i146
 
5.1%
.146
 
5.1%
m146
 
5.1%
Other values (16)949
33.3%

Interactions

2022-05-09T21:23:24.562369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:08.576958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:12.695941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:14.369968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:16.096414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.620059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:19.971862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:21.374228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:23.062414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.225390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:09.647779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:13.357361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.073190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:16.770091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:18.345615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:20.468887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.026890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:23.719760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.326399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:10.035313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:13.472030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.176332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:16.868383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:18.533505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:20.581506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.130364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:23.815953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.421252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:10.387161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:13.579978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.271607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:16.961919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:18.710863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:20.682941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.224654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:23.910230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.512343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:10.740667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:13.685312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.383693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.051900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:18.879097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:20.770970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.458697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:23.999671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.822979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:11.275978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:13.925781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.616872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.250338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:19.273386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:20.943453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.685639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:24.194557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:25.924854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:11.577404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:14.046672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.714230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.344673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:19.435054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:21.040695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.782959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:24.295393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:26.033098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:11.893708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:14.151800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.813019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.437442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:19.605455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:21.140949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.876969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:24.389019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:26.117624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:12.258464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:14.255327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:15.903679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:17.521792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:19.781260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:21.262878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:22.968664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:23:24.469365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:23:32.597460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:23:32.867366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:23:32.990411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:23:33.146637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:23:33.365637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:23:26.290820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:23:27.004000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:23:27.226217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:23:27.346500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
02007756https://www.tvmaze.com/episodes/2007756/stand-up-autsajd-1x11-pavel-dedisev-17-minut-serebraПавел Дедищев "17 минут серебра"1.011.0regular2020-12-2912:002020-12-29T00:00:00+00:0020.0Nonenan51065https://www.tvmaze.com/shows/51065/stand-up-autsajdStand Up АутсайдVarietyRussian[]Ended40.028.02020-10-132020-12-31https://premier.one/show/137343.0nan<p>Solo performances of stand-up comedians from the underground and popular TV and Internet projects. Each new release is a new concert with its own atmosphere and humor.</p>1.616719e+09https://api.tvmaze.com/episodes/1977902
11987860https://www.tvmaze.com/episodes/1987860/blic-krik-1x12-12-nurlan-saburov-t-fest-garik-oganisan-rustam-reptiloid-emir-kasokov#12: НУРЛАН САБУРОВ, T-Fest, ГАРИК ОГАНИСЯН, РУСТАМ РЕПТИЛОИД, ЭМИР КАШОКОВ1.012.0regular2020-12-2912:002020-12-29T00:00:00+00:0030.0Nonenan52044https://www.tvmaze.com/shows/52044/blic-krikБлиц-крикVarietyRussian['Comedy']Running30.032.02019-08-20nanhttps://www.youtube.com/channel/UCrMU71nSWHAmpb1-3wEtW8g/videos18.0nan<p>A humorous show where participants ' knowledge is as important as humor! The game consists of three rounds. The host reads out the beginning of a quote, poem, or fact, and the contestant must finish them either funny or correctly! The winner is the one who turns out to be the most intelligent and fun!</p>1.645301e+09https://api.tvmaze.com/episodes/2015818
22008031https://www.tvmaze.com/episodes/2008031/lab-s-antonom-belaevym-2x10-therr-maitzTherr Maitz2.010.0regular2020-12-29nan2020-12-29T00:00:00+00:0034.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/294/737210.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/294/737210.jpg'}nan52933https://www.tvmaze.com/shows/52933/lab-s-antonom-belaevymLAB с Антоном БеляевымDocumentaryRussian['Music']Running26.025.02019-12-17nanhttps://hd.kinopoisk.ru/film/41a971dd517a30f9b86d20def219c32615.0nan<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>1.641808e+09https://api.tvmaze.com/episodes/1964000
31964569https://www.tvmaze.com/episodes/1964569/core-sense-1x13-episode-13Episode 131.013.0regular2020-12-2910:002020-12-29T02:00:00+00:0024.0Nonenan51336https://www.tvmaze.com/shows/51336/core-senseCore SenseAnimationChinese['Action', 'Anime', 'Science-Fiction']Running24.024.02020-10-13nanhttps://www.bilibili.com/bangumi/media/md2822306437.0nan<p>The power of beginnings, the energy of the core stone; one may find it good, one may find it evil. During a normal investigation, Yue Juntian finds himself drawn into the battle between the 'beginnings' of Yun City; Jiang Xin arrives in Yun City to stop Li Zunyuan's plan to take over. The two influence each other - one solves the mystery of their birth, the other redeems themselves. Together, they oppose Li Zunyuan.<br /> </p>1.604587e+09https://api.tvmaze.com/episodes/1995405
42052513https://www.tvmaze.com/episodes/2052513/wu-shen-zhu-zai-1x88-episode-88Episode 881.088.0regular2020-12-2910:002020-12-29T02:00:00+00:008.0Nonenan54033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese['Action', 'Adventure', 'Anime', 'Fantasy']Running8.08.02020-03-08nanhttps://v.qq.com/detail/m/7q544xyrava3vxf.html76.0nan<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>1.649423e+09https://api.tvmaze.com/episodes/2007760
51993658https://www.tvmaze.com/episodes/1993658/7-days-of-romance-2x03-episode-3Episode 32.03.0regular2020-12-29nan2020-12-29T03:00:00+00:0015.0Nonenan44276https://www.tvmaze.com/shows/44276/7-days-of-romance7 Days of RomanceScriptedKorean['Drama', 'Romance']EndedNaN15.02019-10-082021-01-20nan82.0nan<p>Da Eun works part-time and Kim Byul is an idol in her 5th years since debut. These two girls who look alike decide to change each other's lives just for 7 days. It tells the romantic encounters of these 2 girls.</p>1.650034e+09https://api.tvmaze.com/episodes/1985789
62096302https://www.tvmaze.com/episodes/2096302/no-turning-back-romance-1x07-771.07.0regular2020-12-29nan2020-12-29T03:00:00+00:0012.0Nonenan55002https://www.tvmaze.com/shows/55002/no-turning-back-romanceNo Turning Back RomanceScriptedKorean[]EndedNaN12.02020-12-082021-01-06nan20.0nan<p>A teen romance of So Dam, a sixteen-year-old girl, who has never dated before but she receives her first-ever love confession from a mysterious boy. She is looking for the boy who secretly confessed to her while she was asleep on her desk. The clues include a male voice, mango fruit scent and gym uniform. She must piece the puzzle to find that person among the likely candidates that include hot shots Park Ji Hoo, Jeong Han Kyul, and Joo In Hyuk.</p>1.621617e+09https://api.tvmaze.com/episodes/2039622
72324423https://www.tvmaze.com/episodes/2324423/unique-lady-2x11-episode-11Episode 112.011.0regular2020-12-2912:002020-12-29T04:00:00+00:0040.0Nonenan41490https://www.tvmaze.com/shows/41490/unique-ladyUnique LadyScriptedChinese['Drama', 'Comedy', 'Romance']Ended38.042.02019-01-172021-01-07http://www.iqiyi.com/a_19rrhvpyyp.html68.0nan<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>1.651863e+09https://api.tvmaze.com/episodes/2039623
82324424https://www.tvmaze.com/episodes/2324424/unique-lady-2x12-episode-12Episode 122.012.0regular2020-12-2912:002020-12-29T04:00:00+00:0038.0Nonenan41490https://www.tvmaze.com/shows/41490/unique-ladyUnique LadyScriptedChinese['Drama', 'Comedy', 'Romance']Ended38.042.02019-01-172021-01-07http://www.iqiyi.com/a_19rrhvpyyp.html68.0nan<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>1.651863e+09https://api.tvmaze.com/episodes/2324427
92068352https://www.tvmaze.com/episodes/2068352/doomsday-awakening-2x03-episode-3Episode 32.03.0regular2020-12-29nan2020-12-29T04:00:00+00:0015.0Nonenan48673https://www.tvmaze.com/shows/48673/doomsday-awakeningDoomsday AwakeningAnimationChinese['Action', 'Anime', 'Science-Fiction', 'War']Running15.015.02018-05-24nanhttps://v.qq.com/detail/j/jaqpncskrgv28oo.html56.0nannan1.618077e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimeimagesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
631989717https://www.tvmaze.com/episodes/1989717/you-never-eat-alone-1x02-episode-2Episode 21.02.0regular2020-12-2922:002020-12-29T15:00:00+00:0045.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/728205.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/728205.jpg'}nan52327https://www.tvmaze.com/shows/52327/you-never-eat-aloneYou Never Eat AloneScriptedThai['Drama', 'Comedy', 'Food']Ended45.045.02020-12-222021-03-09https://aisplay.ais.co.th/62.0nan<p>When hunger and loneliness are the same as home. Because eating alone, it's a big deal. The operation to find someone to eat also began...</p>1.619472e+09https://api.tvmaze.com/episodes/1998679
642165009https://www.tvmaze.com/episodes/2165009/all-about-android-2020-12-29-2020-in-the-rear-view2020 in the Rear View2020.052.0regular2020-12-29nan2020-12-29T17:00:00+00:0090.0Nonenan17633https://www.tvmaze.com/shows/17633/all-about-androidAll About AndroidNewsEnglish[]Running90.090.02011-03-29nanhttps://twit.tv/shows/all-about-android30.0nan<p><b>All About Android </b>delivers everything you want to know about Android each week -- the biggest news, freshest hardware, best apps and geekiest how-to's -- with Android enthusiasts Jason Howell, Florence Ion, Ron Richards, and a variety of special guests along the way.</p>1.650312e+09https://api.tvmaze.com/episodes/1998680
651968004https://www.tvmaze.com/episodes/1968004/a-teacher-1x10-episode-10Episode 101.010.0regular2020-12-29nan2020-12-29T17:00:00+00:0030.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/289/724614.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/289/724614.jpg'}<p>Claire and Eric have seemingly moved on with their lives, but a chance encounter brings new truths to light.<br /> </p>38339https://www.tvmaze.com/shows/38339/a-teacherA TeacherScriptedEnglish['Drama']EndedNaN27.02020-11-102020-12-29https://www.hulu.com/series/a-teacher-1c871218-05b1-4c66-a22f-260b2cb9bbf991.0nan<p><b>A Teacher</b> examines the complexities and consequences of an illegal relationship between a female teacher, Claire and her male high school student, Eric. Dissatisfied in their own lives, Claire and Eric discover an undeniable escape in each other, but their relationship accelerates faster than anticipated and the permanent damage becomes impossible to ignore.</p>1.637345e+09https://api.tvmaze.com/episodes/1998681
662192627https://www.tvmaze.com/episodes/2192627/diti-proti-zirok-2x13-vypusk-13-artem-fedeckij-masa-efrosinina-amador-lopesВыпуск 13 (Артем Федецкий, Маша Ефросинина, Амадор Лопес)2.013.0regular2020-12-2919:002020-12-29T17:00:00+00:0090.0Nonenan44675https://www.tvmaze.com/shows/44675/diti-proti-zirokДіти проти зірокGame ShowUkrainian['Action', 'Family']Running90.090.02019-09-25nanhttps://novy.tv/ua/deti-protiv-zvezd/21.0{'name': 'Ukraine', 'code': 'UA', 'timezone': 'Europe/Zaporozhye'}nan1.640953e+09https://api.tvmaze.com/episodes/1998682
671979306https://www.tvmaze.com/episodes/1979306/familiekokkene-1x06-vegetarfestVegetarfest1.06.0regular2020-12-2918:002020-12-29T17:00:00+00:0060.0Nonenan49903https://www.tvmaze.com/shows/49903/familiekokkeneFamiliekokkeneRealityNorwegian['Food']Running60.060.02020-11-27nanhttps://tv.nrk.no/serie/familiekokkene3.0nan<p>Six families with different backgrounds and a big interest in food compete to become Norways best home chef.</p>1.607887e+09https://api.tvmaze.com/episodes/1998683
681977421https://www.tvmaze.com/episodes/1977421/goede-tijden-slechte-tijden-31x70-aflevering-6325Aflevering 632531.070.0regular2020-12-2920:002020-12-29T19:00:00+00:0023.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727577.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727577.jpg'}nan2504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.025.01990-10-01nanhttp://gtst.nl/#!/77.0nannan1.651839e+09https://api.tvmaze.com/episodes/2189555
691994076https://www.tvmaze.com/episodes/1994076/celebrity-a-21st-century-story-1x01-episode-one-ordinary-peopleEpisode One Ordinary People1.01.0regular2020-12-2921:002020-12-29T21:00:00+00:0060.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727732.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727732.jpg'}<p>The first episode examines how the Beckhams changed the model of the celebrity power couple, while the rise of reality TV led to a gigantic influx of people gaining overnight fame, fed by a press that saw an easy source of material. The programme also examines how leaked sex tapes changed the nature of celebrity scandal.</p>52661https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-storyCelebrity: A 21st-Century StoryDocumentaryEnglish['History']To Be Determined60.060.02020-12-29nanhttps://www.bbc.co.uk/programmes/m000qsk128.0nan<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>1.611355e+09https://api.tvmaze.com/episodes/2176148
701994077https://www.tvmaze.com/episodes/1994077/celebrity-a-21st-century-story-1x02-episode-two-trainwreckEpisode Two Trainwreck1.02.0regular2020-12-2921:002020-12-29T21:00:00+00:0060.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727731.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727731.jpg'}<p>Charting the story of celebrity in the late noughties, this film looks at how different groups cashed in on the public's insatiable appetite for access to the famous.</p>52661https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-storyCelebrity: A 21st-Century StoryDocumentaryEnglish['History']To Be Determined60.060.02020-12-29nanhttps://www.bbc.co.uk/programmes/m000qsk128.0nan<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>1.611355e+09https://api.tvmaze.com/episodes/1949329
711994078https://www.tvmaze.com/episodes/1994078/celebrity-a-21st-century-story-1x03-episode-three-lust-for-likesEpisode Three Lust for Likes1.03.0regular2020-12-2921:002020-12-29T21:00:00+00:0060.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727729.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727729.jpg'}<p>This film tells the story of how celebrities capitalised on a digital revolution in order to sidestep the traditional routes to fame.</p>52661https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-storyCelebrity: A 21st-Century StoryDocumentaryEnglish['History']To Be Determined60.060.02020-12-29nanhttps://www.bbc.co.uk/programmes/m000qsk128.0nan<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>1.611355e+09https://api.tvmaze.com/episodes/1949330
721994079https://www.tvmaze.com/episodes/1994079/celebrity-a-21st-century-story-1x04-episode-four-power-grabEpisode Four Power Grab1.04.0regular2020-12-2921:002020-12-29T21:00:00+00:0060.0{'medium': 'https://static.tvmaze.com/uploads/images/medium_landscape/291/727727.jpg', 'original': 'https://static.tvmaze.com/uploads/images/original_untouched/291/727727.jpg'}<p>Charting the last five tumultuous years, this final episode takes us to the end of 2020 and interrogates the methods used by celebrities to get everything they want.</p>52661https://www.tvmaze.com/shows/52661/celebrity-a-21st-century-storyCelebrity: A 21st-Century StoryDocumentaryEnglish['History']To Be Determined60.060.02020-12-29nanhttps://www.bbc.co.uk/programmes/m000qsk128.0nan<p>The world of celebrity has been transformed since the turn of the century, driven by some of the most dramatic technological and cultural developments in recent times.</p><p>This series charting the explosion in celebrity culture.</p>1.611355e+09https://api.tvmaze.com/episodes/1949331